5 citations · 6 across the 6 of their papers we have counts for
6 papers
Online Distributional Prediction via Latent Cluster Geometry Under Drift and Corruption
Navyansh Mahla, Prateek Chanda, Ganesh Ramakrishnan
Online learning in non-stationary streams is often formulated as tracking a point estimate, although many applications require predicting the full data-generating distribution. We…
Federated Cross-Modal Style-Aware Prompt Generation
Suraj Prasad, Navyansh Mahla, Sunny Gupta +1
Prompt learning has propelled vision-language models like CLIP to excel in diverse tasks, making them ideal for federated learning due to computational efficiency. However, convent…
Sequential Compression Layers for Efficient Federated Learning in Foundational Models
Navyansh Mahla, Sunny Gupta, Amit Sethi
Federated Learning (FL) has gained popularity for fine-tuning large language models (LLMs) across multiple nodes, each with its own private data. While LoRA has been widely adopted…
Exploring Gradient Subspaces: Addressing and Overcoming LoRA's Limitations in Federated Fine-Tuning of Large Language Models
Navyansh Mahla, Kshitij Sharad Jadhav, Ganesh Ramakrishnan
Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, particularly in task generalization for both text and vision data. While fine-tuning…
MedSAGa: Few-shot Memory Efficient Medical Image Segmentation using Gradient Low-Rank Projection in SAM
Navyansh Mahla, Annie D'souza, Shubh Gupta +2
The application of large-scale models in medical image segmentation demands substantial quantities of meticulously annotated data curated by experts along with high computational r…
FESS Loss: Feature-Enhanced Spatial Segmentation Loss for Optimizing Medical Image Analysis
Charulkumar Chodvadiya, Navyansh Mahla, Kinshuk Gaurav Singh +1
Medical image segmentation is a critical process in the field of medical imaging, playing a pivotal role in diagnosis, treatment, and research. It involves partitioning of an image…